Fan Wu

Artificial Intelligence · with Chen · 2015–2019

A researcher in the Science corpus: 2 papers; trusts 34 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 74 citations into it; #20,373 of 35,345 by trust here, #28,481 by citations
• Computer Vision and Pattern Recognition — 19 citations into it; #12,680 of 21,575 by trust here, #19,310 by citations
• Optimization & Training — 11 citations into it
• Safety, Robustness & Interpretability — 9 citations into it
• Model Architectures — 6 citations into it

Whom Fan Wu trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Matthew D. Zeiler
4. Kaiming He
5. Yann LeCun
6. Rob Fergus
7. Yoshua Bengio
8. Vinod Nair
9. Ilya Sutskever
10. Diederik P. Kingma

Who trusts Fan Wu overall

1. Diego Marcos
2. Sander Dieleman
3. Junying Li
4. Xiuyuan Cheng
5. Qiang Qiu
6. Michele Volpi
7. Jeffrey De Fauw
8. Zichen Yang

Papers from Fan Wu's perspective

Ordered by how strongly the researchers Fan Wu trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. ImageNet Large Scale Visual Recognition Challenge
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Flip-Rotate-Pooling Convolution and Split Dropout on Convolution Neural Networks for Image Classification (2015) — 30 citations
• Secure Federated Submodel Learning (2019) — 35 citations